Agentic education · Learning science

Actionable companion · Self-regulation & metacognition

Learning with agency: Incorporating an Intelligent Tutoring System Into a Game-Based Auditory Rehabilitation Training for Adult Cochlear Implant Recipients: Algorithm Development and Validation

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

Read original paperSearch paper agentCC BY 4.0

Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background Cochlear implants are implanted hearing devices; instead of amplifying sounds like common hearing aids, this technology delivers preprocessed sound information directly to the hearing (ie, auditory) nerves. After surgery and the first cochlear implant activation, patients must practice interpreting the new auditory sensations, especially for language comprehension. This rehabilitation process is accompanied by hearing therapy through face-to-face training with a therapist, self-directed training, and computer-based auditory training. Objective In general, self-directed, computer-based auditory training tasks have already shown advantages. However, compliance of cochlear implant recipients is still a major factor, especially for self-directed training at home. Hence, we aimed to explore the combination of 2 techniques to enhance learner motivation in this context: adaptive learning (in the form of an intelligent tutoring system) and game-based learning (in the form of a serious game). Methods Following the suggestions of the evidence-centered design framework, a domain analysis of hearing therapy was conducted, allowing us to partially describe human hearing skill as a probabilistic competence model (Bayesian network). We developed an algorithm that uses such a model to estimate the current competence level of a patient and create training recommendations. For training, our developed task system was based on 7 language comprehension task types that act as a blueprint for generating tasks of diverse difficulty automatically. To achieve this, 1053 audio assets with meta-information labels were created. We embedded the adaptive task system into a graphic novel–like mobile serious game. German-speaking cochlear implant recipients used the system during a feasibility study for 4 weeks. Results The 23 adult participants (20 women; 3 men) fulfilled 2259 tasks. In total, 2004 (90.5%) tasks were solved correctly, and 255 (9.5%) tasks were solved incorrectly. A generalized additive model analysis of these tasks indicated that the system adapted to the estimated competency levels of the cochlear implant recipients more quickly in the beginning than at the end. Compared with a uniform distribution of all task types, the recommended task types differed (χ²6=86.713; P<.001), indicating that the system selected specific task types for each patient. This is underlined by the identified categories for the error proportions of the task types. Conclusions This contribution demonstrates the feasibility of combining an intelligent tutoring system with a serious game in cochlear implant rehabilitation therapies. The findings presented here could lead to further advances in cochlear implant care and aural rehabilitation in general. Trial Registration German Clinical Trials Register (DRKS) DRKS00022860; https://drks.de/search/en/trial/DRKS00022860 Keywords: cochlear implant, eHealth, evidence-centered design, hearing rehabilitation, adaptive learning, intelligent tutoring system, game-based learning

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Design requires full-text appraisal

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

The system design started from the perspective of assessments owing to the behavior of hearing therapists, who included exercises and tasks to assess hearing competencies and adapt the rehabilitation program. The ECD was followed because this methodology supported the design of assessments in educational systems and the inference of relevant competencies (Textbox 1).

Limitations and uncertainty · source passage

First, compared with studies in educational science where the ECD framework originated, the total number of participants in this study was relatively low because of the available funding for the conducted feasibility study. Hence, cautious generalization of the results is required. Nevertheless, the results presented in this publication are a valuable base for a formal sample size calculation (eg, Cochran’s sample size formula) for future studies. Second, only cochlear implant recipients from the Department of Otorhinolaryngology at the University Hospital Düsseldorf volunteered in this study, raising the possibility of an existing self-selection bias. This bias might also include side effects from different types of cochlear implant sound processors used by the cochlear implant recipients. Finally, we did not evaluate the competencies of the participating cochlear implant recipients with another measurement instrument due to the lack of standardized tests for the specific subcompetencies. The study was conducted to answer specific RQs and comply strictly with the EU GDPR. Hence, further post hoc analysis can only be applied to the specific tracked variables defined at the beginning of the project. Therefore, for example, questions regarding the preferred game mode of the cochlear implant recipients must be answered in future studies.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

  • Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Florian Gnadlinger; Maika Werminghaus; André Selmanagić; Tim Filla; Jutta G Richter; Simone Kriglstein; Thomas Klenzner. Incorporating an Intelligent Tutoring System Into a Game-Based Auditory Rehabilitation Training for Adult Cochlear Implant Recipients: Algorithm Development and Validation. JMIR Serious Games (2024-12-03). https://doi.org/10.2196/55231

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.